Correlation between Quantitative and Qualitative Analysis on Image Quality of Digital Dental X-ray Images

Signal to Noise Ratio (SNR) is a quantitative method to measure the effectiveness of contrast enhancement methods. The question is whether this measurement is consistent with a qualitative evaluation of a medical expert. Biomedical Research, including dentistry cases, usually involves medical experts to do the interpretation, and finalize the diagnosis. However, the subjective evaluations create differences of interpretation. There are two factors contributing to the low quality of dental radiographs—the first is because of the nature of the image acquisition process, and the second is the low X-ray dose usage. Due to that, research related to contrast enhancement algorithms is an accepted image processing method to assist medical officers in doing interpretation that is more reliable. Thus, this work presents an analysis of contrast enhancement algorithms (CEA) applied to digital intraoral radiograph images. The enhancement methods used in this study are CEAs, namely: adaptive histogram equalization (AHE), contrast limited adaptive histogram equalization (CLAHE), and sharp contrast limited adaptive histogram equalization (SCLAHE). The objective of this work is to find a correlation between the dentist evaluation and SNR values in determining the image quality based on the rate of abnormalities detected. Fifty-six original intraoral digital dental X-ray images are collected from the Faculty of Dentistry, Universiti Teknologi MARA Malaysia. These images are processed with the mentioned CEAs, resulting in a total of onehundred sixty-six observations. These images are compared against the original images. The method of assessment is through questionnaires where all images are arranged randomly. The observations focused on identifying widened periodontal ligament space (widened PDLs), periapical radiolucency (PR) and loss of lamina dura (loss LD) abnormalities. These are among the main signs of periapical disease. The results show that the SNR value does not reflect the dentist’s evaluation in terms of image quality. The SNR values of CLAHE are able to precede other methods. However, SCLAHE is better than the rest based on dentists’ evaluations for widened PDLs abnormality.

[1]  Hazem M. El-Bakry,et al.  A new technique for detecting dental diseases by using high speed neuro-computers , 2008 .

[2]  David P Sarment,et al.  Variation in periodontal diagnosis and treatment planning among clinical instructors. , 2005, Journal of dental education.

[3]  Byung Gil Choi,et al.  Image Quality Improvement after Implementation of a CT Accreditation Program , 2010, Korean journal of radiology.

[4]  A. Halse,et al.  Diagnosing periapical lesions--disagreement and borderline cases. , 2002, International endodontic journal.

[5]  P F van der Stelt,et al.  Diagnostic confidence and the accuracy of treatment decisions for radiopaque periapical lesions. , 1995, International endodontic journal.

[6]  Stephen Marshall,et al.  Medical image enhancement using threshold decomposition driven adaptive morphological filter , 2008, 2008 16th European Signal Processing Conference.

[7]  Youngjoon Han,et al.  Image Contrast Enhancement based Sub-histogram Equalization Technique without Over-equalization Noise , 2009 .

[8]  Hisashi Tanaka,et al.  Usefulness of contrast-enhanced three-dimensional MR angiography using time-resolved imaging of contrast kinetics applied to description of Extracranial Arteriovenous Malformations: initial experience. , 2012, European Journal of Radiology.

[9]  T Sund,et al.  Sliding window adaptive histogram equalization of intraoral radiographs: effect on image quality. , 2006, Dento maxillo facial radiology.

[10]  R K Taira,et al.  Image content extraction: application to MR images of the brain. , 2001, Radiographics : a review publication of the Radiological Society of North America, Inc.

[11]  Rafael C. González,et al.  Local Determination of a Moving Contrast Edge , 1985, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[12]  Roland Frankenberger,et al.  Nanohybrid vs. fine hybrid composite in extended Class II cavities after six years. , 2011, Dental materials : official publication of the Academy of Dental Materials.

[13]  Tao Jianhua,et al.  A Fast Implementation of Adaptive Histogram Equalization , 2006, 2006 8th international Conference on Signal Processing.

[14]  J Samson,et al.  Periapical central giant cell granuloma misdiagnosed as odontogenic cyst. , 2006, International endodontic journal.

[15]  Jonas Capelli,et al.  Matrix metalloproteinases and chemokines in the gingival crevicular fluid during orthodontic tooth movement. , 2011, European journal of orthodontics.

[16]  Mariana Carmen Nicolae,et al.  COMPARATIVE APPROACH FOR SPECKLE REDUCTION IN MEDICAL ULTRASOUND IMAGES , 2010 .

[17]  E. Vêncio,et al.  Mesenchymal chondrosarcoma in the mandible: report of a case with cytological findings. , 2011, Journal of oral science.

[18]  S. Suresh Kumar,et al.  A NOVEL APPROACH FOR CONTRAST ENHANCEMENT BASED ON HISTOGRAM EQUALIZATION FOLLOWED BY MEDIAN FILTER , 2009 .

[19]  Julio Cezar de Melo Castilho,et al.  INFLUENCE OF IMAGE FILTERS ON THE REPRODUCIBILITY OF MEASUREMENTS OF ALVEOLAR BONE LOSS , 2006, Journal of applied oral science : revista FOB.

[20]  Aboul Ella Hassanien,et al.  A Fuzzy-Rule Based Algorithm for Contrast Enhancement of Mammograms Breast Masses , 2004 .

[21]  R Martina,et al.  A clinical comparison of the efficacy and efficiency of two professional prophylaxis procedures in orthodontic patients. , 1999, European journal of orthodontics.

[22]  I. Tsalafoutas,et al.  Exploring the useful exposure range limits of three intraoral image receptors for various tube potential, tube current and exposure time settings , 2011 .

[23]  Mojdeh Mehdizadeh,et al.  Study of effect of Adaptive Histogram Equalization on image quality in digital preapical image in pre apex area. , 2009 .

[24]  Bradley M. Hemminger,et al.  Contrast Limited Adaptive Histogram Equalization image processing to improve the detection of simulated spiculations in dense mammograms , 1998, Journal of Digital Imaging.

[25]  M. A. Yousuf,et al.  An Effective Image Contrast Enhancement Method Using Global Histogram Equalization , 2010 .

[26]  Moh'd Belal Al-Zoubi,et al.  New spatial filters for image enhancement and noise removal , 2006 .

[27]  Ya-qin Zhu,et al.  Six cases report of differential diagnosis of periapical diseases , 2011, International Journal of Oral Science.

[28]  Mohd Nasir Taib,et al.  Performance of Compound Enhancement Algorithms on Dental Radiograph Images , 2011 .

[29]  Jianhua Tao,et al.  A Fast Implementation of Adaptive Histogram Equalization , 2006 .

[30]  K. Kamburoğlu,et al.  Interpretation of chemically created periapical lesions using 2 different dental cone-beam computerized tomography units, an intraoral digital sensor, and conventional film. , 2009, Oral surgery, oral medicine, oral pathology, oral radiology, and endodontics.

[31]  Nor Ashidi Mat Isa,et al.  A proposed system for edge mammogram image , 2010 .

[32]  J. Declerck,et al.  The subjective image quality of direct digital and conventional panoramic radiography , 2000, Clinical Oral Investigations.

[33]  Timothy J. Terry,et al.  Region-Based Adaptive Contrast Enhancement , 2002 .

[34]  Rudolf Gmür,et al.  Quantitative detection of Porphyromonas gingivalis fimA genotypes in dental plaque. , 2004, FEMS microbiology letters.

[35]  R. Langlais,et al.  Principles of Dental Imaging , 1997 .

[36]  Michael Allen,et al.  Parallel programming: techniques and applications using networked workstations and parallel computers , 1998 .

[37]  Javier E Botero,et al.  Subgingival microbiota in peri-implant mucosa lesions and adjacent teeth in partially edentulous patients. , 2005, Journal of periodontology.

[38]  Berit I Ardlin,et al.  Transformation-toughened zirconia for dental inlays, crowns and bridges: chemical stability and effect of low-temperature aging on flexural strength and surface structure. , 2002, Dental materials : official publication of the Academy of Dental Materials.

[39]  Robert A. Hummel,et al.  Image Enhancement by Histogram transformation , 1975 .

[40]  R. Willers,et al.  Subjective image quality of digitally filtered radiographs acquired by the Dürr Vistascan system compared with conventional radiographs. , 2006, Oral surgery, oral medicine, oral pathology, oral radiology, and endodontics.

[41]  S. Arumuga Perumal,et al.  Contrast Improvement of Radiographic Images in Spatial Domain by Edge Preserving Filters , 2010 .

[42]  Mumtaj Begam,et al.  Enhancement of Bone Fracture Image Using Filtering Techniques , 2009 .

[43]  Karel J. Zuiderveld,et al.  Contrast Limited Adaptive Histogram Equalization , 1994, Graphics Gems.

[44]  Ghassan Hamarneh,et al.  A New Preprocessing Filter for Digital Mammograms , 2010, ICISP.

[45]  E Alpöz,et al.  Perception of anatomical structures in digitally filtered and conventional panoramic radiographs: a clinical evaluation. , 2010, Dento maxillo facial radiology.

[46]  M. N. Taib,et al.  The effect of sharp contrast-limited adaptive histogram equalization (SCLAHE) on Intra-oral dental radiograph images , 2010, 2010 IEEE EMBS Conference on Biomedical Engineering and Sciences (IECBES).

[47]  Ron Kimmel,et al.  Images as Embedded Maps and Minimal Surfaces: Movies, Color, Texture, and Volumetric Medical Images , 2000, International Journal of Computer Vision.

[48]  K. Kamburoğlu,et al.  A comparison of the diagnostic accuracy of in vivo and in vitro photostimulable phosphor digital images in the detection of occlusal caries lesions. , 2010, Dento maxillo facial radiology.

[49]  Tianxi Cai,et al.  Contrast opacification using a reduced volume of iodinated contrast material and low peak kilovoltage in pulmonary CT angiography: Objective and subjective evaluation. , 2010, AJR. American journal of roentgenology.

[50]  R M Henkelman,et al.  Image resolution and signal-to-noise ratio requirements for MR imaging of degenerative cartilage. , 1997, AJR. American journal of roentgenology.

[51]  C Siew,et al.  Pharmacokinetics of bisphenol A released from a dental sealant. , 2000, Journal of the American Dental Association.

[52]  P. Scheunders,et al.  Quantification and improvement of the signal-to-noise ratio in a magnetic resonance image acquisition procedure. , 1996, Magnetic resonance imaging.

[53]  Savita Gupta,et al.  Region Based Adaptive Contrast Enhancement of Medical X-Ray Images , 2011, 2011 5th International Conference on Bioinformatics and Biomedical Engineering.

[54]  G. Zentai,et al.  Signal-to-Noise and Contrast Ratio Enhancements by Quasi-Monochromatic Imaging , 2011, IEEE Transactions on Instrumentation and Measurement.

[55]  Giang Nguyen,et al.  A prospective evaluation of dose reduction and image quality in chest CT using adaptive statistical iterative reconstruction. , 2010, AJR. American journal of roentgenology.

[56]  J. Todd.  Book Review: Digital image processing (second edition). By R. C. Gonzalez and P. Wintz, Addison-Wesley, 1987. 503 pp. Price: £29.95. (ISBN 0-201-11026-1) , 1988 .

[57]  Sheng-Jyh Wang,et al.  Generalized Histogram Equalization Based on Local Characteristics , 2006, 2006 International Conference on Image Processing.

[58]  N. Khalid,et al.  Fish Bone Impaction Using Adaptive Histogram Equalization (AHE) , 2010, 2010 Second International Conference on Computer Research and Development.

[59]  Perry Sprawls,et al.  Physical principles of medical imaging , 1987 .